Technical Paper

Using CAA and DFM scoring to improve manufacturing success

Using CAA and DFM scoring to improve manufacturing success

Critical area analysis and design for manufacturing scoring both offer designers actionable information they can use to improve their designs to prevent low-yield issues in the foundry. At the same time, they provide the foundry with information they can use for process improvement. Learn how fabless designers, foundries, and integrated device manufacturers can all benefit from addressing manufacturing susceptibilities in designs before tape-out to achieve the best results in both yield and product quality.

Share

Related resources

Machine learning-powered etch bias prediction for etch retargeting flow enhancement
Technical Paper

Machine learning-powered etch bias prediction for etch retargeting flow enhancement

The paper proposes a machine learning (ML) based approach to predict etch bias, which can replace the traditional rule-based etch bias tables

Guided random synthetic layout generation and machine-learning based defect prediction for leading edge technology node development
Technical Paper

Guided random synthetic layout generation and machine-learning based defect prediction for leading edge technology node development

Combining synthetic layout generation and machine learning accelerates advanced semiconductor development. It enables early identification of process hotspots and defect prediction, improving efficiency and reducing costs.

A study on the improvement of machine learning (ML)-based defect prediction model reflecting process variations
Technical Paper

A study on the improvement of machine learning (ML)-based defect prediction model reflecting process variations

This paper presents an ML-based risk pattern predictor that combines pattern segmentation, Greedy sampling, and unbiased statistics to find defects and improve yield.